Overview

Dataset statistics

Number of variables24
Number of observations11810
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 MiB
Average record size in memory399.5 B

Variable types

Numeric9
Categorical11
Text4

Alerts

LAND has constant value "11"Constant
UJAHR has constant value "2020"Constant
BEZ is highly overall correlated with LOR and 1 other fieldsHigh correlation
LOR is highly overall correlated with BEZ and 1 other fieldsHigh correlation
LOR_ab_2021 is highly overall correlated with BEZ and 1 other fieldsHigh correlation
OBJECTID is highly overall correlated with UMONATHigh correlation
UMONAT is highly overall correlated with OBJECTIDHigh correlation
UKATEGORIE is highly imbalanced (59.7%)Imbalance
IstGkfz is highly imbalanced (80.2%)Imbalance
OBJECTID has unique valuesUnique
USTUNDE has 129 (1.1%) zerosZeros
UART has 1766 (15.0%) zerosZeros

Reproduction

Analysis started2024-06-14 14:27:46.276687
Analysis finished2024-06-14 14:28:39.623044
Duration53.35 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

OBJECTID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct11810
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122320.45
Minimum3187
Maximum146070
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.4 KiB
2024-06-14T16:28:40.426781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3187
5-th percentile5326.45
Q1137079.25
median140031.5
Q3142983.75
95-th percentile145345.55
Maximum146070
Range142883
Interquartile range (IQR)5904.5

Descriptive statistics

Standard deviation46594.604
Coefficient of variation (CV)0.38092243
Kurtosis2.4345134
Mean122320.45
Median Absolute Deviation (MAD)2952.5
Skewness-2.098782
Sum1.4446045 × 109
Variance2.1710571 × 109
MonotonicityStrictly increasing
2024-06-14T16:28:41.320874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3187 1
 
< 0.1%
142003 1
 
< 0.1%
141994 1
 
< 0.1%
141995 1
 
< 0.1%
141996 1
 
< 0.1%
141997 1
 
< 0.1%
141998 1
 
< 0.1%
141999 1
 
< 0.1%
142000 1
 
< 0.1%
142001 1
 
< 0.1%
Other values (11800) 11800
99.9%
ValueCountFrequency (%)
3187 1
< 0.1%
3198 1
< 0.1%
3215 1
< 0.1%
3224 1
< 0.1%
3241 1
< 0.1%
3252 1
< 0.1%
3274 1
< 0.1%
3290 1
< 0.1%
3299 1
< 0.1%
3303 1
< 0.1%
ValueCountFrequency (%)
146070 1
< 0.1%
146068 1
< 0.1%
146065 1
< 0.1%
146062 1
< 0.1%
146061 1
< 0.1%
146059 1
< 0.1%
146058 1
< 0.1%
146052 1
< 0.1%
146051 1
< 0.1%
146049 1
< 0.1%

LAND
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size588.3 KiB
11
11810 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters23620
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 11810
100.0%

Length

2024-06-14T16:28:42.078665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T16:28:42.623988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
11 11810
100.0%

Most occurring characters

ValueCountFrequency (%)
1 23620
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23620
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23620
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23620
100.0%

BEZ
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5752752
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.4 KiB
2024-06-14T16:28:43.136720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q38
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5014191
Coefficient of variation (CV)0.62802624
Kurtosis-1.1112003
Mean5.5752752
Median Absolute Deviation (MAD)3
Skewness0.31972293
Sum65844
Variance12.259936
MonotonicityNot monotonic
2024-06-14T16:28:43.868928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 1832
15.5%
4 1386
11.7%
2 1174
9.9%
3 1085
9.2%
7 1061
9.0%
9 858
7.3%
6 843
7.1%
8 822
7.0%
12 806
6.8%
5 734
6.2%
Other values (2) 1209
10.2%
ValueCountFrequency (%)
1 1832
15.5%
2 1174
9.9%
3 1085
9.2%
4 1386
11.7%
5 734
6.2%
6 843
7.1%
7 1061
9.0%
8 822
7.0%
9 858
7.3%
10 559
 
4.7%
ValueCountFrequency (%)
12 806
6.8%
11 650
5.5%
10 559
4.7%
9 858
7.3%
8 822
7.0%
7 1061
9.0%
6 843
7.1%
5 734
6.2%
4 1386
11.7%
3 1085
9.2%

LOR
Real number (ℝ)

HIGH CORRELATION 

Distinct444
Distinct (%)3.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5620349.6
Minimum1011101
Maximum12304314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.4 KiB
2024-06-14T16:28:44.547240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1011101
5-th percentile1011302
Q12050802
median5020524
Q38041035
95-th percentile12214125
Maximum12304314
Range11293213
Interquartile range (IQR)5990233

Descriptive statistics

Standard deviation3522024.5
Coefficient of variation (CV)0.62665576
Kurtosis-1.0717425
Mean5620349.6
Median Absolute Deviation (MAD)2989880
Skewness0.33889368
Sum6.6370709 × 1010
Variance1.2404656 × 1013
MonotonicityNot monotonic
2024-06-14T16:28:45.357273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1011303 134
 
1.1%
1033203 107
 
0.9%
7040403 99
 
0.8%
11030721 94
 
0.8%
2020204 90
 
0.8%
2030302 89
 
0.8%
2050602 88
 
0.7%
5010314 86
 
0.7%
1011301 85
 
0.7%
7050501 84
 
0.7%
Other values (434) 10853
91.9%
ValueCountFrequency (%)
1011101 40
0.3%
1011102 58
0.5%
1011103 32
 
0.3%
1011104 28
 
0.2%
1011105 46
0.4%
1011201 61
0.5%
1011202 61
0.5%
1011203 34
 
0.3%
1011204 65
0.6%
1011301 85
0.7%
ValueCountFrequency (%)
12304314 27
0.2%
12304313 23
0.2%
12302212 12
0.1%
12302211 22
0.2%
12302110 22
0.2%
12302109 5
 
< 0.1%
12302108 8
 
0.1%
12302107 14
0.1%
12301206 7
 
0.1%
12301205 18
0.2%

LOR_ab_2021
Real number (ℝ)

HIGH CORRELATION 

Distinct537
Distinct (%)4.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5865560.3
Minimum1100101
Maximum12601236
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.4 KiB
2024-06-14T16:28:46.214416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1100101
5-th percentile1100309.4
Q12500834
median5200528
Q38401241
95-th percentile12200308
Maximum12601236
Range11501135
Interquartile range (IQR)5900407

Descriptive statistics

Standard deviation3499177.6
Coefficient of variation (CV)0.59656324
Kurtosis-1.077024
Mean5865560.3
Median Absolute Deviation (MAD)2899887
Skewness0.30696759
Sum6.9266401 × 1010
Variance1.2244244 × 1013
MonotonicityNot monotonic
2024-06-14T16:28:47.168555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100310 138
 
1.2%
1300836 107
 
0.9%
4100101 103
 
0.9%
1100102 103
 
0.9%
1100206 96
 
0.8%
2200210 90
 
0.8%
1100308 85
 
0.7%
1100309 81
 
0.7%
5100316 80
 
0.7%
1300733 74
 
0.6%
Other values (527) 10852
91.9%
ValueCountFrequency (%)
1100101 40
 
0.3%
1100102 103
0.9%
1100103 32
 
0.3%
1100104 28
 
0.2%
1100205 57
0.5%
1100206 96
0.8%
1100207 69
0.6%
1100308 85
0.7%
1100309 81
0.7%
1100310 138
1.2%
ValueCountFrequency (%)
12601236 14
 
0.1%
12601235 22
0.2%
12601134 8
 
0.1%
12601133 5
 
< 0.1%
12601032 12
 
0.1%
12601031 22
0.2%
12500930 38
0.3%
12500929 7
 
0.1%
12500928 18
0.2%
12500927 12
 
0.1%

UJAHR
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size611.4 KiB
2020
11810 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters47240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 11810
100.0%

Length

2024-06-14T16:28:47.884827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T16:28:48.314106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2020 11810
100.0%

Most occurring characters

ValueCountFrequency (%)
2 23620
50.0%
0 23620
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 23620
50.0%
0 23620
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 23620
50.0%
0 23620
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 23620
50.0%
0 23620
50.0%

UMONAT
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7767146
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.4 KiB
2024-06-14T16:28:48.836340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.191813
Coefficient of variation (CV)0.47099711
Kurtosis-0.96514476
Mean6.7767146
Median Absolute Deviation (MAD)2
Skewness-0.243377
Sum80033
Variance10.18767
MonotonicityNot monotonic
2024-06-14T16:28:49.420163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 1407
11.9%
9 1340
11.3%
6 1248
10.6%
10 1220
10.3%
7 1128
9.6%
5 897
7.6%
1 862
7.3%
11 835
7.1%
2 793
6.7%
4 745
6.3%
Other values (2) 1335
11.3%
ValueCountFrequency (%)
1 862
7.3%
2 793
6.7%
3 665
5.6%
4 745
6.3%
5 897
7.6%
6 1248
10.6%
7 1128
9.6%
8 1407
11.9%
9 1340
11.3%
10 1220
10.3%
ValueCountFrequency (%)
12 670
5.7%
11 835
7.1%
10 1220
10.3%
9 1340
11.3%
8 1407
11.9%
7 1128
9.6%
6 1248
10.6%
5 897
7.6%
4 745
6.3%
3 665
5.6%

USTUNDE
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.523793
Minimum0
Maximum23
Zeros129
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size92.4 KiB
2024-06-14T16:28:50.305644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median14
Q317
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.6844163
Coefficient of variation (CV)0.34638331
Kurtosis-0.11674792
Mean13.523793
Median Absolute Deviation (MAD)3
Skewness-0.40017211
Sum159716
Variance21.943756
MonotonicityNot monotonic
2024-06-14T16:28:50.891556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
15 1007
 
8.5%
16 980
 
8.3%
14 961
 
8.1%
17 946
 
8.0%
13 893
 
7.6%
18 838
 
7.1%
12 759
 
6.4%
11 672
 
5.7%
8 627
 
5.3%
19 618
 
5.2%
Other values (14) 3509
29.7%
ValueCountFrequency (%)
0 129
 
1.1%
1 74
 
0.6%
2 60
 
0.5%
3 44
 
0.4%
4 43
 
0.4%
5 133
 
1.1%
6 254
2.2%
7 547
4.6%
8 627
5.3%
9 602
5.1%
ValueCountFrequency (%)
23 139
 
1.2%
22 228
 
1.9%
21 237
 
2.0%
20 413
3.5%
19 618
5.2%
18 838
7.1%
17 946
8.0%
16 980
8.3%
15 1007
8.5%
14 961
8.1%

UWOCHENTAG
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0611346
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.4 KiB
2024-06-14T16:28:51.552274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8024611
Coefficient of variation (CV)0.44383191
Kurtosis-1.0783919
Mean4.0611346
Median Absolute Deviation (MAD)1
Skewness0.01302917
Sum47962
Variance3.2488662
MonotonicityNot monotonic
2024-06-14T16:28:52.060995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 2021
17.1%
3 1968
16.7%
5 1934
16.4%
2 1906
16.1%
6 1789
15.1%
7 1261
10.7%
1 931
7.9%
ValueCountFrequency (%)
1 931
7.9%
2 1906
16.1%
3 1968
16.7%
4 2021
17.1%
5 1934
16.4%
6 1789
15.1%
7 1261
10.7%
ValueCountFrequency (%)
7 1261
10.7%
6 1789
15.1%
5 1934
16.4%
4 2021
17.1%
3 1968
16.7%
2 1906
16.1%
1 931
7.9%

UKATEGORIE
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size576.8 KiB
3
10023 
2
1742 
1
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11810
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 10023
84.9%
2 1742
 
14.8%
1 45
 
0.4%

Length

2024-06-14T16:28:52.770281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T16:28:53.370470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 10023
84.9%
2 1742
 
14.8%
1 45
 
0.4%

Most occurring characters

ValueCountFrequency (%)
3 10023
84.9%
2 1742
 
14.8%
1 45
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 10023
84.9%
2 1742
 
14.8%
1 45
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 10023
84.9%
2 1742
 
14.8%
1 45
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 10023
84.9%
2 1742
 
14.8%
1 45
 
0.4%

UART
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.446232
Minimum0
Maximum9
Zeros1766
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size92.4 KiB
2024-06-14T16:28:53.904620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q35
95-th percentile6
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1973193
Coefficient of variation (CV)0.63760052
Kurtosis-1.0658592
Mean3.446232
Median Absolute Deviation (MAD)1
Skewness-0.18718336
Sum40700
Variance4.8282122
MonotonicityNot monotonic
2024-06-14T16:28:54.557012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 4632
39.2%
2 2102
17.8%
0 1766
 
15.0%
6 1283
 
10.9%
1 975
 
8.3%
3 629
 
5.3%
4 173
 
1.5%
8 116
 
1.0%
9 109
 
0.9%
7 25
 
0.2%
ValueCountFrequency (%)
0 1766
 
15.0%
1 975
 
8.3%
2 2102
17.8%
3 629
 
5.3%
4 173
 
1.5%
5 4632
39.2%
6 1283
 
10.9%
7 25
 
0.2%
8 116
 
1.0%
9 109
 
0.9%
ValueCountFrequency (%)
9 109
 
0.9%
8 116
 
1.0%
7 25
 
0.2%
6 1283
 
10.9%
5 4632
39.2%
4 173
 
1.5%
3 629
 
5.3%
2 2102
17.8%
1 975
 
8.3%
0 1766
 
15.0%

UTYP1
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8519052
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.4 KiB
2024-06-14T16:28:55.100179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9291363
Coefficient of variation (CV)0.50082653
Kurtosis-1.3742068
Mean3.8519052
Median Absolute Deviation (MAD)1
Skewness0.22452961
Sum45491
Variance3.7215668
MonotonicityNot monotonic
2024-06-14T16:28:55.686774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 2909
24.6%
6 2568
21.7%
3 2482
21.0%
7 1113
 
9.4%
5 1007
 
8.5%
1 977
 
8.3%
4 754
 
6.4%
ValueCountFrequency (%)
1 977
 
8.3%
2 2909
24.6%
3 2482
21.0%
4 754
 
6.4%
5 1007
 
8.5%
6 2568
21.7%
7 1113
 
9.4%
ValueCountFrequency (%)
7 1113
 
9.4%
6 2568
21.7%
5 1007
 
8.5%
4 754
 
6.4%
3 2482
21.0%
2 2909
24.6%
1 977
 
8.3%

ULICHTVERH
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size576.8 KiB
0
8969 
2
2163 
1
 
678

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11810
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
0 8969
75.9%
2 2163
 
18.3%
1 678
 
5.7%

Length

2024-06-14T16:28:56.291892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T16:28:56.770245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 8969
75.9%
2 2163
 
18.3%
1 678
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 8969
75.9%
2 2163
 
18.3%
1 678
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8969
75.9%
2 2163
 
18.3%
1 678
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8969
75.9%
2 2163
 
18.3%
1 678
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8969
75.9%
2 2163
 
18.3%
1 678
 
5.7%

IstRad
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size576.8 KiB
0
6701 
1
5109 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11810
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6701
56.7%
1 5109
43.3%

Length

2024-06-14T16:28:57.186818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T16:28:57.616297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 6701
56.7%
1 5109
43.3%

Most occurring characters

ValueCountFrequency (%)
0 6701
56.7%
1 5109
43.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6701
56.7%
1 5109
43.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6701
56.7%
1 5109
43.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6701
56.7%
1 5109
43.3%

IstPKW
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size576.8 KiB
1
9378 
0
2432 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11810
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 9378
79.4%
0 2432
 
20.6%

Length

2024-06-14T16:28:58.320153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T16:28:58.783519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 9378
79.4%
0 2432
 
20.6%

Most occurring characters

ValueCountFrequency (%)
1 9378
79.4%
0 2432
 
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 9378
79.4%
0 2432
 
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 9378
79.4%
0 2432
 
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 9378
79.4%
0 2432
 
20.6%

IstFuss
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size576.8 KiB
0
10414 
1
1396 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11810
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10414
88.2%
1 1396
 
11.8%

Length

2024-06-14T16:28:59.384130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T16:28:59.860057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 10414
88.2%
1 1396
 
11.8%

Most occurring characters

ValueCountFrequency (%)
0 10414
88.2%
1 1396
 
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10414
88.2%
1 1396
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10414
88.2%
1 1396
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10414
88.2%
1 1396
 
11.8%

IstKrad
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size576.8 KiB
0
10013 
1
1797 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11810
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 10013
84.8%
1 1797
 
15.2%

Length

2024-06-14T16:29:00.553884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T16:29:01.016526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 10013
84.8%
1 1797
 
15.2%

Most occurring characters

ValueCountFrequency (%)
0 10013
84.8%
1 1797
 
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10013
84.8%
1 1797
 
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10013
84.8%
1 1797
 
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10013
84.8%
1 1797
 
15.2%

IstGkfz
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size576.8 KiB
0
11448 
1
 
362

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11810
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11448
96.9%
1 362
 
3.1%

Length

2024-06-14T16:29:01.461154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T16:29:01.945042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 11448
96.9%
1 362
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 11448
96.9%
1 362
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11448
96.9%
1 362
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11448
96.9%
1 362
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11448
96.9%
1 362
 
3.1%

IstSonstige
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size576.8 KiB
0
10283 
1
1527 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11810
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10283
87.1%
1 1527
 
12.9%

Length

2024-06-14T16:29:02.569226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T16:29:03.085278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 10283
87.1%
1 1527
 
12.9%

Most occurring characters

ValueCountFrequency (%)
0 10283
87.1%
1 1527
 
12.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10283
87.1%
1 1527
 
12.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10283
87.1%
1 1527
 
12.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10283
87.1%
1 1527
 
12.9%

USTRZUSTAND
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size576.8 KiB
0
9061 
1
2728 
2
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11810
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 9061
76.7%
1 2728
 
23.1%
2 21
 
0.2%

Length

2024-06-14T16:29:03.690339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T16:29:04.227967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 9061
76.7%
1 2728
 
23.1%
2 21
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 9061
76.7%
1 2728
 
23.1%
2 21
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9061
76.7%
1 2728
 
23.1%
2 21
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9061
76.7%
1 2728
 
23.1%
2 21
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11810
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9061
76.7%
1 2728
 
23.1%
2 21
 
0.2%
Distinct11716
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size690.3 KiB
2024-06-14T16:29:05.113624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.841321
Min length6

Characters and Unicode

Total characters128036
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11633 ?
Unique (%)98.5%

Sample

1st row802599,5332
2nd row791264,2064
3rd row792294,8083
4th row801024,2746
5th row791889,2861
ValueCountFrequency (%)
793340,975 4
 
< 0.1%
808418,573 3
 
< 0.1%
795336,5269 3
 
< 0.1%
795668,9128 3
 
< 0.1%
796184,661 3
 
< 0.1%
795052,64 3
 
< 0.1%
794958,1529 3
 
< 0.1%
797749,362 3
 
< 0.1%
797848,686 3
 
< 0.1%
807934,6072 3
 
< 0.1%
Other values (11706) 11779
99.7%
2024-06-14T16:29:06.786865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 16951
13.2%
9 15890
12.4%
8 14622
11.4%
, 11809
9.2%
0 11775
9.2%
1 9945
7.8%
2 9540
7.5%
3 9500
7.4%
5 9476
7.4%
4 9356
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 128036
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 16951
13.2%
9 15890
12.4%
8 14622
11.4%
, 11809
9.2%
0 11775
9.2%
1 9945
7.8%
2 9540
7.5%
3 9500
7.4%
5 9476
7.4%
4 9356
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 128036
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 16951
13.2%
9 15890
12.4%
8 14622
11.4%
, 11809
9.2%
0 11775
9.2%
1 9945
7.8%
2 9540
7.5%
3 9500
7.4%
5 9476
7.4%
4 9356
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 128036
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 16951
13.2%
9 15890
12.4%
8 14622
11.4%
, 11809
9.2%
0 11775
9.2%
1 9945
7.8%
2 9540
7.5%
3 9500
7.4%
5 9476
7.4%
4 9356
7.3%
Distinct11714
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size690.8 KiB
2024-06-14T16:29:07.779816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.887976
Min length7

Characters and Unicode

Total characters128587
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11629 ?
Unique (%)98.5%

Sample

1st row5821795,373
2nd row5824629,955
3rd row5823598,115
4th row5827862,923
5th row5824003,006
ValueCountFrequency (%)
5829718,481 4
 
< 0.1%
5829732,336 3
 
< 0.1%
5820877,047 3
 
< 0.1%
5825654,926 3
 
< 0.1%
5824034,295 3
 
< 0.1%
5830195,038 3
 
< 0.1%
5822305,299 3
 
< 0.1%
5833744,859 3
 
< 0.1%
5827184,91 3
 
< 0.1%
5829198,169 3
 
< 0.1%
Other values (11704) 11779
99.7%
2024-06-14T16:29:09.409437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 20201
15.7%
8 20168
15.7%
2 15098
11.7%
, 11794
9.2%
3 11090
8.6%
1 9853
7.7%
7 8568
6.7%
6 8529
6.6%
9 8226
6.4%
4 8063
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 128587
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 20201
15.7%
8 20168
15.7%
2 15098
11.7%
, 11794
9.2%
3 11090
8.6%
1 9853
7.7%
7 8568
6.7%
6 8529
6.6%
9 8226
6.4%
4 8063
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 128587
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 20201
15.7%
8 20168
15.7%
2 15098
11.7%
, 11794
9.2%
3 11090
8.6%
1 9853
7.7%
7 8568
6.7%
6 8529
6.6%
9 8226
6.4%
4 8063
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 128587
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 20201
15.7%
8 20168
15.7%
2 15098
11.7%
, 11794
9.2%
3 11090
8.6%
1 9853
7.7%
7 8568
6.7%
6 8529
6.6%
9 8226
6.4%
4 8063
 
6.3%
Distinct11712
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size690.9 KiB
2024-06-14T16:29:10.318087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.890262
Min length8

Characters and Unicode

Total characters128614
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11627 ?
Unique (%)98.5%

Sample

1st row13,45500598
2nd row13,29111618
3rd row13,30534822
4th row13,43737099
5th row13,29974796
ValueCountFrequency (%)
13,54012811 5
 
< 0.1%
13,32610463 4
 
< 0.1%
13,34627672 3
 
< 0.1%
13,36833544 3
 
< 0.1%
13,38582718 3
 
< 0.1%
13,36034106 3
 
< 0.1%
13,38574131 3
 
< 0.1%
13,53953897 3
 
< 0.1%
13,35902459 3
 
< 0.1%
13,34902078 3
 
< 0.1%
Other values (11702) 11777
99.7%
2024-06-14T16:29:11.976028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 24522
19.1%
1 20442
15.9%
4 11979
9.3%
, 11810
9.2%
2 9939
7.7%
5 9476
 
7.4%
6 8446
 
6.6%
8 8413
 
6.5%
9 8400
 
6.5%
7 8216
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 128614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 24522
19.1%
1 20442
15.9%
4 11979
9.3%
, 11810
9.2%
2 9939
7.7%
5 9476
 
7.4%
6 8446
 
6.6%
8 8413
 
6.5%
9 8400
 
6.5%
7 8216
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 128614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 24522
19.1%
1 20442
15.9%
4 11979
9.3%
, 11810
9.2%
2 9939
7.7%
5 9476
 
7.4%
6 8446
 
6.6%
8 8413
 
6.5%
9 8400
 
6.5%
7 8216
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 128614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 24522
19.1%
1 20442
15.9%
4 11979
9.3%
, 11810
9.2%
2 9939
7.7%
5 9476
 
7.4%
6 8446
 
6.6%
8 8413
 
6.5%
9 8400
 
6.5%
7 8216
 
6.4%
Distinct11711
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size690.8 KiB
2024-06-14T16:29:12.833601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.88823
Min length7

Characters and Unicode

Total characters128590
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11623 ?
Unique (%)98.4%

Sample

1st row52,4623009
2nd row52,49387309
3rd row52,48407191
4th row52,517556
5th row52,48791866
ValueCountFrequency (%)
52,53837732 4
 
< 0.1%
52,50107765 3
 
< 0.1%
52,52566473 3
 
< 0.1%
52,53724347 3
 
< 0.1%
52,45081783 3
 
< 0.1%
52,48503473 3
 
< 0.1%
52,46948207 3
 
< 0.1%
52,57339236 3
 
< 0.1%
52,51474176 3
 
< 0.1%
52,54111107 3
 
< 0.1%
Other values (11701) 11779
99.7%
2024-06-14T16:29:14.509289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 26811
20.8%
2 20142
15.7%
4 13095
10.2%
, 11810
9.2%
3 8594
 
6.7%
1 8521
 
6.6%
6 8395
 
6.5%
9 8268
 
6.4%
8 8138
 
6.3%
7 7757
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 128590
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 26811
20.8%
2 20142
15.7%
4 13095
10.2%
, 11810
9.2%
3 8594
 
6.7%
1 8521
 
6.6%
6 8395
 
6.5%
9 8268
 
6.4%
8 8138
 
6.3%
7 7757
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 128590
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 26811
20.8%
2 20142
15.7%
4 13095
10.2%
, 11810
9.2%
3 8594
 
6.7%
1 8521
 
6.6%
6 8395
 
6.5%
9 8268
 
6.4%
8 8138
 
6.3%
7 7757
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 128590
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 26811
20.8%
2 20142
15.7%
4 13095
10.2%
, 11810
9.2%
3 8594
 
6.7%
1 8521
 
6.6%
6 8395
 
6.5%
9 8268
 
6.4%
8 8138
 
6.3%
7 7757
 
6.0%

Interactions

2024-06-14T16:28:30.228217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:27:53.262546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:27:58.972748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:04.123315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:08.686774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:12.664966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:16.871518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:20.933616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:25.439687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:31.021958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:27:54.571012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:27:59.500182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:04.610103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:09.113092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:13.170100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:17.315026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:21.458685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:25.960927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:31.696003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:27:55.123075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:00.143157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:05.122159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:09.608324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:13.622672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:17.736866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:21.924065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:26.359178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:32.424896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:27:55.562988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:00.850827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:05.973193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:10.034955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:14.066279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:18.190926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:22.442702image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:26.756745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:32.920782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:27:56.038232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:01.498195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:06.472856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:10.447848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:14.556435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:18.652930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:22.912245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:27.175866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:33.331220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:27:56.647894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:02.099387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:06.942098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:10.900659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:15.015224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:19.051474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:23.366585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:27.621452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:33.988913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:27:57.333279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:02.600478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:07.351816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:11.349403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:15.486484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:19.484519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:23.792556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:28.519253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:34.677830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:27:57.933215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:03.081487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:07.801479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:11.830153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:15.938576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:19.901841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:24.374529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:28.998443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:35.204055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:27:58.403727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:03.604627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:08.176015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:12.252810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:16.388173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:20.395137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:24.888408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-14T16:28:29.486507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-06-14T16:29:15.028968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
BEZIstFussIstGkfzIstKradIstPKWIstRadIstSonstigeLORLOR_ab_2021OBJECTIDUARTUKATEGORIEULICHTVERHUMONATUSTRZUSTANDUSTUNDEUTYP1UWOCHENTAG
BEZ1.0000.0270.0420.0240.1060.1830.0360.9930.9920.0100.0150.0580.048-0.0040.026-0.0420.0120.002
IstFuss0.0271.0000.0310.1240.1360.1660.0110.0140.014-0.0010.4790.1040.077-0.0380.0320.0290.0880.009
IstGkfz0.0420.0311.0000.0440.1000.0560.0240.0300.030-0.057-0.0260.1030.041-0.0180.018-0.0840.044-0.017
IstKrad0.0240.1240.0441.0000.1250.3180.0690.0020.0020.018-0.1510.0760.0140.0250.0270.038-0.021-0.014
IstPKW0.1060.1360.1000.1251.0000.2420.2860.0650.065-0.0110.1460.0950.000-0.0180.0420.0240.0920.009
IstRad0.1830.1660.0560.3180.2421.0000.141-0.112-0.1120.052-0.0320.0440.1040.0250.1080.006-0.1670.002
IstSonstige0.0360.0110.0240.0690.2860.1411.000-0.010-0.010-0.008-0.0820.0000.0110.0080.009-0.0560.0660.006
LOR0.9930.0140.0300.0020.065-0.112-0.0101.0001.0000.0130.0160.0600.049-0.0030.025-0.0430.0110.002
LOR_ab_20210.9920.0140.0300.0020.065-0.112-0.0101.0001.0000.0130.0160.0580.045-0.0040.026-0.0420.0110.002
OBJECTID0.010-0.001-0.0570.018-0.0110.052-0.0080.0130.0131.0000.0050.0100.0230.7370.000-0.028-0.0520.010
UART0.0150.479-0.026-0.1510.146-0.032-0.0820.0160.0160.0051.0000.1200.108-0.0330.078-0.004-0.191-0.004
UKATEGORIE0.0580.1040.1030.0760.0950.0440.0000.0600.0580.0100.1201.0000.0270.0260.021-0.0110.062-0.000
ULICHTVERH0.0480.0770.0410.0140.0000.1040.0110.0490.0450.0230.1080.0271.0000.0140.1520.304-0.0660.030
UMONAT-0.004-0.038-0.0180.025-0.0180.0250.008-0.003-0.0040.737-0.0330.0260.0141.0000.238-0.032-0.0040.001
USTRZUSTAND0.0260.0320.0180.0270.0420.1080.0090.0250.0260.0000.0780.0210.1520.2381.000-0.015-0.0560.006
USTUNDE-0.0420.029-0.0840.0380.0240.006-0.056-0.043-0.042-0.028-0.004-0.0110.304-0.032-0.0151.0000.0360.005
UTYP10.0120.0880.044-0.0210.092-0.1670.0660.0110.011-0.052-0.1910.062-0.066-0.004-0.0560.0361.0000.004
UWOCHENTAG0.0020.009-0.017-0.0140.0090.0020.0060.0020.0020.010-0.004-0.0000.0300.0010.0060.0050.0041.000

Missing values

2024-06-14T16:28:36.126613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-14T16:28:38.154064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-14T16:28:39.219191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

OBJECTIDLANDBEZLORLOR_ab_2021UJAHRUMONATUSTUNDEUWOCHENTAGUKATEGORIEUARTUTYP1ULICHTVERHIstRadIstPKWIstFussIstKradIstGkfzIstSonstigeUSTRZUSTANDLINREFXLINREFYXGCSWGS84YGCSWGS84
031871188010510.08100521.02020111423600100101802599,53325821795,37313,4550059852,4623009
131981144041137.04400727.0202012329120000101791264,20645824629,95513,2911161852,49387309
232151144041239.04400830.0202017130110000011792294,80835823598,11513,3053482252,48407191
332241122040502.02400521.02020122621520100000801024,27465827862,92313,4373709952,517556
432411144041137.04400727.02020117130120001001791889,28615824003,00613,2997479652,48791866
532521174051655.04501153.02020114632600100000794432,6455823090,62813,3362916452,47837396
632741177010104.07100205.02020113230400010010796076,51215825501,67513,3625679552,49909824
732901177040401.07400721.02020115721100100000797817,49415822978,1713,3858842352,47553062
832991133020209.03200206.02020113532600100000797966,87135834349,8513,3982670852,57738163
933031111044102.01400940.0202019135300100000795861,67325832069,45813,3652568952,55808889
OBJECTIDLANDBEZLORLOR_ab_2021UJAHRUMONATUSTUNDEUWOCHENTAGUKATEGORIEUARTUTYP1ULICHTVERHIstRadIstPKWIstFussIstKradIstGkfzIstSonstigeUSTRZUSTANDLINREFXLINREFYXGCSWGS84YGCSWGS84
11800146049111010020415.010200422.020201216320720100001812061,32215831698,3513,6031071452,54572302
118011460511177040403.07400823.020201211435200100011797358,52195821998,95813,3782736752,46700306
118021460521113071536.03701556.020201216332620100001799079,69715829018,85113,4098381852,52898708
118031460581122010104.02100106.020201216336420110001799823,15555825723,8213,4177960452,49904396
118041460591122020203.02200209.02020123135220100000797507,61465824015,47913,3822607852,48499784
118051460611177040403.07400823.02020127436210110001797339,74475821935,19413,3779412652,46644169
11806146062111212231101.012400722.020201217335320100001791814,83335837953,17113,3109147652,61301792
118071460651111011306.01100313.020201214335300100010799900,80485826514,72513,4196487952,50609051
118081460681111011204.01100207.020201216336220110001797982,89665826897,22213,3918174152,51057
118091460701111033101.01300732.020201214432200100100797307,12855832290,57413,3867139952,5592837